How does this differ from standard exploration?

Standard exploration often seeks novelty or randomness. Curiosity-driven learning seeks 'interestingness,' which is defined as the gap between what is already known and what is completely random/unlearnable.

How does this skill handle random noise?

Because random noise is incompressible and does not lead to a better world model, the curiosity reward for noise is zero, preventing the agent from becoming obsessed with unpredictable data.

What is Curiosity-Driven Learning?

It is an intrinsic motivation framework where an agent is rewarded for seeking out data that improves its internal world model, specifically through the discovery of compressible patterns.

What is the 'Compression Progress' metric?

Compression Progress is the difference in the description length of a dataset before and after a model update. A positive value indicates the agent has learned something new and structure-based.

Curiosity-Driven Learning Agent

Name: Curiosity-Driven Learning Agent
Author: plurigrid

byplurigrid

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Data Science & ML

Implements intrinsic motivation for AI agents by rewarding compression progress and the discovery of learnable patterns.

About

This skill integrates Jürgen Schmidhuber's formal theory of curiosity into agentic workflows, enabling Claude to prioritize actions that improve its internal world model. Unlike standard exploration strategies that focus on pure novelty, this skill utilizes 'Compression Progress'—the mathematical reduction in the description length of historical data—to drive learning. It allows agents to distinguish between meaningful, learnable regularities and useless random noise, making it essential for developing autonomous systems that exhibit developmental behavior, avoid boredom, and self-evolve through intrinsic motivation.

Key Features

Provides a framework for estimating the learnability of future states
Supports interaction entropy metrics to measure the 'interestingness' of new data
Calculates intrinsic rewards based on Compression Progress (L(t-1) - L(t))
Integrates with GF(3) Triads for compositional coherence in complex agent architectures
Differentiates between learnable patterns and stochastic noise to solve the 'noisy TV' problem
2 GitHub stars

Use Cases

Building autonomous RL agents for environments with sparse or delayed extrinsic rewards
Researching Artificial General Intelligence (AGI) and self-evolving agent behaviors
Optimizing data collection by focusing on high-information, compressible patterns

About

Key Features

Provides a framework for estimating the learnability of future states
Supports interaction entropy metrics to measure the 'interestingness' of new data
Calculates intrinsic rewards based on Compression Progress (L(t-1) - L(t))
Integrates with GF(3) Triads for compositional coherence in complex agent architectures
Differentiates between learnable patterns and stochastic noise to solve the 'noisy TV' problem
2 GitHub stars

Use Cases

Building autonomous RL agents for environments with sparse or delayed extrinsic rewards
Researching Artificial General Intelligence (AGI) and self-evolving agent behaviors
Optimizing data collection by focusing on high-information, compressible patterns